AI-Driven Financial Analytics for Enhancing Corporate Financial Performance and Risk Management

Abstract

This study, titled "AI-Driven Financial Analytics for Enhancing Corporate Financial Performance and Risk Management," evaluates analytics application allocations, earnings forecasting Root Mean Squared Error (RMSE) accuracy, Return on Equity (ROE) expansion, financial distress risk mitigation, and financial feasibility of enterprise AI financial engines in corporate finance. Modern enterprises face complex macroeconomic volatility, where predictive cash flow forecasting represents 42% and enterprise risk detection accounts for 28% of financial analytics applications. A five-year project lifecycle (2021-2025) of an AIdriven financial analytics platform is evaluated using capital budgeting parameters: Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PBP), and Benefit-Cost Ratio (BCR). Quantitative analysis indicates that deploying deep learning analytics reduces earnings forecasting error to 0.5% RMSE compared to 8.5% under traditional budgeting. Managed corporate capital expansion to 145,000 Crores elevates Return on Equity (ROE) to 32.8%, expanding AI analytics adoption to 94.8%, compressing financial distress risk to 0.5%, and shortening financial close cycle time to 0.8 days by 2025. The financial model yields a positive NPV of 284.5 Crores and an IRR of 38.6%, far exceeding the 10% discount hurdle rate. The study concludes that investing in AI financial analytics platforms is highly viable, boosting corporate governance, risk management, and shareholder value creation. Keywords: AI Financial Analytics, Corporate Performance, Risk Management, Cash Flow Forecasting, Return on Equity (ROE), Financial Distress Risk, Capital Budgeting.